Artificial Intelligence (AI) is a new technological science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. AI is also a branch of computer science, seeking to understand the essence of intelligence and to produce a new type of intelligent machine that responds in a manner similar to human intelligence. Researches of AI may include robotics, speech recognition, image recognition, natural language processing and expert systems.
In the semantic understanding technology, semantic information of a query is usually expressed in a structured form of intentions and slots. Specifically, the slot refers to some semantic fragments and the intention refers to a demand word i.e., a main word of the query. For example, if a query is “find a funny English movie without paying”, “movie” is referred to a demand word to express the intention, that is, “movie” is the main word of the query. Semantic fragments such as “without paying”, “funny” and “English” are used to limit the main word “movie”.
Segmentation boundaries of the semantic fragments directly affect the slot recognition results, which indirectly affects satisfaction of a user on the query results. If a segmentation size of the semantic fragment is too small, different meanings may be resulted. For example, if “without paying” is separated into “without” and “paying”, the slot may be likely to be identified as “paying”, resulting in an opposite result.
If the segmentation size of the semantic fragment is too large, a slot dimension may not be matched with a resource dimension, and thus no result meeting the conditions of the query can be retrieved. For example, if “funny English” is not separated and directly used to carry out a retrieve, it is likely that no result meeting conditions of such semantic fragment is obtained, as “funny” and “English” belong to two dimensions in the knowledge base resources. Therefore, it is of great importance to extract the semantic fragments with ideal segmentation boundary.
In the related art, semantic fragment of the query is usually mined with a manually edited template. However, such method needs accumulation edited manually, which wastes human resources, and is not conducive to automation. In addition, because the template is not flexible enough, only the fragment in a fixed format can be identified, thus leading to poor effect of the semantic fragment recognition. Therefore, the results retrieved for the query cannot meet the requirements of the users.